Deployment of a point and line feature localization system for an outdoor agriculture vehicle

This paper presents a perception-based GPS-free approach for localizing a mobile robot in an orchard environment. An extended Kalman filter (EKF) algorithm is presented that uses a wheel odometry prediction step and laser rangefinder update steps. There are two update steps, one that uses measurements to reflective point features and one that uses measurements to linear features formed by tree rows. The features are associated to landmarks in previously surveyed maps. The practical issues of dealing with uncertainty both from the environment and the on-board sensors are discussed and accounted for. The resulting algorithm is demonstrated in over 20km of online operation in a variety of real orchard environments.